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When basic particle swarm optimization algorithm (PSO) is used to resolve some complex problems, its global optimal model usually falls into local optimal value and its local model has slowest convergence velocity in the later stage of evolution. So, a simplified particle swarm optimization algorithm is proposed. Firstly, all particles in whole swarm are divided into three categories, denoted as the...
Classification problem is an important and complex problem in machine learning. Support vector machine (SVM) has recently emerged as a powerful technique for solving problems in classification, but its performance mainly depends on the parameters selection of it. Parameters selection for SVM is very complex in nature and quite hard to solve by conventional optimization techniques such as least Squares...
Because the network intrusion behaviors are characterized with uncertainty, complexity and diversity, a new method based on support vector regression (SVR) and particle swarm optimization algorithm (PSOA) is presented and used for pattern analysis of intrusion detection in this paper. The novel structure model has higher accuracy and faster convergence speed. We construct the network structure, and...
According to characteristics of particle swarm optimization algorithm, a novel particle swarm optimization algorithm based on adaptive space mutation (ASM-PSO) is proposed. During the searching process, the convergence speed and globally convergence ability is greatly improved by the adaptive space mutation based on the variance of the population's fitness. Experiment results show that the new method,...
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